Abstract: Embodiments are directed to providing control functionality of a display of an electronic device. The method includes determining, by the electronic device, an event and partitioning, by the electronic device, a rear side of the electronic device into dynamic tiles based on multiple parameters associated with the display, based on determining the event. The method also includes detecting, by the electronic device, a user gesture on the dynamic tile on the rear side of the electronic device and classifying, by the electronic device, the detected gesture into a gesture category. Further, the method includes determining, by the electronic device, a functionality based on the gesture on the rear side of the electronic device and the corresponding gesture category. The method includes controlling, by the electronic device, the functionality on the display of the electronic device.

Abstract: A method for optical camera quality enhancement is provided. The method includes receiving, by an electronic device, an image frame of a scene including at least one object from a preview field of a camera of the electronic device, determining, by the electronic device, at least one region of the image frame affected by distortion, generating, by the electronic device, a pixel disparity map comprising a refraction coefficient associated with each pixel of the image frame based on the at least one region affected by the distortion, determining, by the electronic device, a plurality of regions on the pixel disparity map, each of the plurality of regions including pixels that have a similar pixel disparity, estimating, by the electronic device, an optimal orientation of the electronic device to minimize the distortion in the image frame based on the plurality of regions on the pixel disparity map, and displaying, by the electronic device, an indication indicating the optimal orientation of the electronic device.

Abstract: A method for applying adaptive zoom on an image is disclosed. The method includes detecting at least one input to perform zoom on the image, determining key pixels in the image, obtaining a plurality of segments by determining sizes of each of the plurality of the segments based on a number of the key pixels in each of the plurality of the segments, determining a priority order for zooming the plurality of the segments based on a density of the key pixels in each of the plurality of the segments, determining a zoom level to be applied on each of the plurality of the segments based on the density of the key pixels in each of the plurality of the segments, and adaptively zooming each of the plurality of the segments based on the zoom level and the priority order.

Abstract: Embodiments are directed to a method and an electronic device (100) for managing audio content delivery by an audio content delivery device (100). The method includes detecting, by the audio content delivery device (100), a buzzing sound in a user's ear during output of an audio content. The audio content delivery device (100) categorizes the buzzing sound, for example, as a subjective buzzing or an objective buzzing. Further, the audio content delivery device (100) computes a user experience score and generates a modified audio content based the user experience score. The modified audio content is based on the buzzing sound and the audio content. The audio content delivery device (100) outputs the modified audio content depending on the user experience score.

Abstract: A method for accidental touch prediction using machine learning (ML) classification is provided. The method includes determining, by an electronic device, a mutual data index of a sensor data using a first ML model. Further, the method includes recognizing whether the sensor data corresponds to an object touch or a non-object touch based on the mutual data index. Further, the method includes performing, by the electronic device, one of detecting that the electronic device is in a pocket mode and providing an object touch notification on a touch screen of the electronic device in response to determining that the sensor data corresponds to the object touch, and recognizing whether the sensor data corresponds to an accidental touch or a non-accidental touch using at least one second ML model based on touch data in response to determining that the sensor data corresponds to the non-object touch.

Abstract: A method for accidental touch prediction using machine learning (ML) classification is provided. The method includes determining, by an electronic device, a mutual data index of a sensor data using a first ML model. Further, the method includes recognizing whether the sensor data corresponds to an object touch or a non-object touch based on the mutual data index. Further, the method includes performing, by the electronic device, one of detecting that the electronic device is in a pocket mode and providing an object touch notification on a touch screen of the electronic device in response to determining that the sensor data corresponds to the object touch, and recognizing whether the sensor data corresponds to an accidental touch or a non-accidental touch using at least one second ML model based on touch data in response to determining that the sensor data corresponds to the non-object touch.